{"id":"https://openalex.org/W4387846639","doi":"https://doi.org/10.1145/3583780.3615073","title":"Targeted Shilling Attacks on GNN-based Recommender Systems","display_name":"Targeted Shilling Attacks on GNN-based Recommender Systems","publication_year":2023,"publication_date":"2023-10-21","ids":{"openalex":"https://openalex.org/W4387846639","doi":"https://doi.org/10.1145/3583780.3615073"},"language":"en","primary_location":{"id":"doi:10.1145/3583780.3615073","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3583780.3615073","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3583780.3615073","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 32nd ACM International Conference on Information and Knowledge Management","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3583780.3615073","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5040745223","display_name":"Sihan Guo","orcid":"https://orcid.org/0009-0009-1378-8937"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Sihan Guo","raw_affiliation_strings":["Beijing University of Posts and Telecommunications, Beijing , China"],"raw_orcid":"https://orcid.org/0009-0009-1378-8937","affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, Beijing , China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Ting Bai","orcid":"https://orcid.org/0000-0001-9607-3551"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ting Bai","raw_affiliation_strings":["Beijing University of Posts and Telecommunications, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-9607-3551","affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5025452586","display_name":"Weihong Deng","orcid":"https://orcid.org/0000-0001-5952-6996"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weihong Deng","raw_affiliation_strings":["Beijing University of Posts and Telecommunications, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-5952-6996","affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I139759216"],"apc_list":null,"apc_paid":null,"fwci":7.127,"has_fulltext":true,"cited_by_count":22,"citation_normalized_percentile":{"value":0.97898269,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"649","last_page":"658"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.9994000196456909,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.9994000196456909,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9939000010490417,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11478","display_name":"Caching and Content Delivery","score":0.9851999878883362,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/recommender-system","display_name":"Recommender system","score":0.8728293180465698},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.836066722869873},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.5554917454719543},{"id":"https://openalex.org/keywords/flexibility","display_name":"Flexibility (engineering)","score":0.4751427173614502},{"id":"https://openalex.org/keywords/collaborative-filtering","display_name":"Collaborative filtering","score":0.44906866550445557},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4449799656867981},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.41557595133781433},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.34036141633987427},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.22122332453727722},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2155706286430359},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.091835618019104}],"concepts":[{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.8728293180465698},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.836066722869873},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.5554917454719543},{"id":"https://openalex.org/C2780598303","wikidata":"https://www.wikidata.org/wiki/Q65921492","display_name":"Flexibility (engineering)","level":2,"score":0.4751427173614502},{"id":"https://openalex.org/C21569690","wikidata":"https://www.wikidata.org/wiki/Q94702","display_name":"Collaborative filtering","level":3,"score":0.44906866550445557},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4449799656867981},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.41557595133781433},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.34036141633987427},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.22122332453727722},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2155706286430359},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.091835618019104},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3583780.3615073","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3583780.3615073","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3583780.3615073","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 32nd ACM International Conference on Information and Knowledge Management","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3583780.3615073","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3583780.3615073","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3583780.3615073","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 32nd ACM International Conference on Information and Knowledge Management","raw_type":"proceedings-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","score":0.5,"display_name":"Peace, Justice and strong institutions"},{"id":"https://metadata.un.org/sdg/10","score":0.49000000953674316,"display_name":"Reduced inequalities"}],"awards":[{"id":"https://openalex.org/G1980180333","display_name":null,"funder_award_id":"62102038,62236003","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5630254371","display_name":null,"funder_award_id":"62236003","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4387846639.pdf","grobid_xml":"https://content.openalex.org/works/W4387846639.grobid-xml"},"referenced_works_count":39,"referenced_works":["https://openalex.org/W1965675974","https://openalex.org/W1977290284","https://openalex.org/W1977556410","https://openalex.org/W1989279326","https://openalex.org/W2025226803","https://openalex.org/W2072651985","https://openalex.org/W2074771540","https://openalex.org/W2075446120","https://openalex.org/W2079466849","https://openalex.org/W2096029105","https://openalex.org/W2122114437","https://openalex.org/W2144194596","https://openalex.org/W2807021761","https://openalex.org/W2886329066","https://openalex.org/W2892160417","https://openalex.org/W2945827670","https://openalex.org/W2971567992","https://openalex.org/W2999213888","https://openalex.org/W3012794253","https://openalex.org/W3012833893","https://openalex.org/W3012846134","https://openalex.org/W3025378468","https://openalex.org/W3034894152","https://openalex.org/W3038342492","https://openalex.org/W3043777434","https://openalex.org/W3045200674","https://openalex.org/W3048511744","https://openalex.org/W3100278010","https://openalex.org/W3100848837","https://openalex.org/W3101291735","https://openalex.org/W3104774544","https://openalex.org/W3104956872","https://openalex.org/W3156766660","https://openalex.org/W3171665710","https://openalex.org/W3182668916","https://openalex.org/W3209941761","https://openalex.org/W4212835176","https://openalex.org/W4226237846","https://openalex.org/W4306789986"],"related_works":["https://openalex.org/W1484355083","https://openalex.org/W2772628444","https://openalex.org/W4220714703","https://openalex.org/W2735929803","https://openalex.org/W2170391450","https://openalex.org/W2098758514","https://openalex.org/W3008845055","https://openalex.org/W2041004656","https://openalex.org/W135044020","https://openalex.org/W4376854386"],"abstract_inverted_index":{"GNN-based":[0,70,175],"recommender":[1,18,71],"systems":[2],"have":[3,24],"shown":[4],"their":[5,144],"vulnerability":[6],"to":[7,23,48,57,121,158],"shilling":[8,15,67,186],"attacks":[9,16,33,68,90],"in":[10,69,173],"recent":[11],"studies.":[12],"By":[13,73],"conducting":[14,135],"on":[17,27,59,91,101,113,138,143,169,184],"systems,":[19],"the":[20,43,76,81,99,105,122,139,150,159,179,185,193],"attackers":[21],"aim":[22],"homogeneous":[25],"impacts":[26],"all":[28],"users.":[29,103,126,164],"However,":[30],"such":[31],"indiscriminate":[32],"suffer":[34],"from":[35],"a":[36,92,114],"waste":[37],"of":[38,80,95,107,124,129,162,181,197],"resources":[39],"because":[40],"even":[41],"if":[42],"target":[44,125,140],"item":[45],"is":[46,118,132],"promoted":[47],"users":[49,96,109,131,141],"who":[50],"are":[51,55,110],"not":[52],"interested,":[53],"they":[54],"unlikely":[56],"click":[58],"them.":[60],"In":[61],"this":[62],"paper,":[63],"we":[64],"conduct":[65,166],"targeted":[66],"systems.":[72],"automatically":[74],"constructing":[75],"features":[77,106,123],"and":[78,152,177,188,195],"edges":[79],"fake":[82,108,130,163],"users,":[83],"our":[84,182,198],"proposed":[85],"framework":[86],"AutoAttack":[87],"achieves":[88],"accurate":[89],"specific":[93],"group":[94],"while":[97],"minimizing":[98],"impact":[100],"non-target":[102],"Specifically,":[104],"generated":[111],"based":[112,142],"similarity":[115],"function,":[116],"which":[117,148],"optimized":[119],"according":[120],"The":[127],"structure":[128],"learned":[133],"by":[134],"spectral":[136],"clustering":[137],"graph":[145],"Laplacian":[146],"matrix,":[147],"contains":[149],"degree":[151],"adjacency":[153],"information":[154],"that":[155],"provides":[156],"guidance":[157],"edge":[160],"generation":[161],"We":[165],"extensive":[167],"experiments":[168],"four":[170],"real-world":[171],"datasets":[172],"different":[174],"RS":[176],"evaluate":[178],"performance":[180],"method":[183],"attack":[187],"recommendation":[189],"tasks":[190],"comprehensively,":[191],"showing":[192],"effectiveness":[194],"flexibility":[196],"framework.":[199]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":12},{"year":2024,"cited_by_count":7}],"updated_date":"2026-07-31T08:31:51.225901","created_date":"2025-10-10T00:00:00"}